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Data Analytics from Enroll-HD, a Global Clinical Research Platform for Huntington's Disease.

ABSTRACT: Background:The study of complex neurodegenerative diseases is moving away from hypothesis-driven biological methods toward large scale multimodal approaches, requiring standardized collaborative efforts. Enroll-HD exemplifies such an integrated clinical research platform, designed and implemented to meet the research and clinical needs of Huntington's disease (HD). The aim of this study was to describe the unique organization of Enroll-HD and report baseline data analyses of its core study. Methods:The Enroll-HD platform incorporates electronic data capture, biosampling, and a longitudinal observational study spanning four continents (ClinicalTrials.gov Identifier: NCT01574053). The primary study population includes HD gene expansion carriers (HDGECs; CAG expansion ?36), subdivided into manifest/premanifest HD. The control population consists of genotype-negative first-degree relatives and family controls not genetically related. The study includes 10 core clinical assessments covering motor, cognitive, and behavioral domains. Results:This data set comprises 1,534 participants (HDGEC = 1,071; controls = 463). Participant retention was high; 42 participants prematurely withdrew from the study. Mean ± standard deviation SD CAG repeat size was 43.5 ± 3.5 for HDGECs and 19.8 ± 3.4 for controls. Motor and behavioral assessments identified numerical differences between controls and HDGECs (manifest > premanifest > controls). Functional and independence assessments were generally similar for the premanifest and control groups with overlap in range of scores obtained. For the majority of cognitive tests, there were large differences between participants with manifest HD and all other groups. Conclusions:These first data from the Enroll-HD clinical research platform demonstrate the maturity and potential of the platform in collecting high-quality, clinically relevant data. Future data sets will be substantially larger as the platform expands longitudinally and regionally.

SUBMITTER: Landwehrmeyer GB

PROVIDER: S-EPMC6174428 | biostudies-literature | 2017 Mar-Apr

REPOSITORIES: biostudies-literature

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Data Analytics from Enroll-HD, a Global Clinical Research Platform for Huntington's Disease.

Landwehrmeyer Georg B GB Fitzer-Attas Cheryl J CJ Giuliano Joseph D JD Gonçalves Nilza N Anderson Karen E KE Cardoso Francisco F Ferreira Joaquim J JJ Mestre Tiago A TA Stout Julie C JC Sampaio Cristina C

Movement disorders clinical practice 20160622 2

<h4>Background</h4>The study of complex neurodegenerative diseases is moving away from hypothesis-driven biological methods toward large scale multimodal approaches, requiring standardized collaborative efforts. Enroll-HD exemplifies such an integrated clinical research platform, designed and implemented to meet the research and clinical needs of Huntington's disease (HD). The aim of this study was to describe the unique organization of Enroll-HD and report baseline data analyses of its core stu ...[more]

PMID: 30363395

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Project description:BackgroundHuntington's disease (HD) is a fatal progressive neurodegenerative disorder caused by the expansion of the polyglutamine repeat region in the huntingtin gene. Although the disease is triggered by the mutation of a single gene, intensive research has linked numerous other genes to its pathogenesis. To obtain a systematic overview of these genes, which may serve as therapeutic targets, CHDI Foundation has recently established the HD Research Crossroads database. With currently over 800 cataloged genes, this web-based resource constitutes the most extensive curation of genes relevant to HD. It provides us with an unprecedented opportunity to survey molecular mechanisms involved in HD in a holistic manner.MethodsTo gain a synoptic view of therapeutic targets for HD, we have carried out a variety of bioinformatical and statistical analyses to scrutinize the functional association of genes curated in the HD Research Crossroads database. In particular, enrichment analyses were performed with respect to Gene Ontology categories, KEGG signaling pathways, and Pfam protein families. For selected processes, we also analyzed differential expression, using published microarray data. Additionally, we generated a candidate set of novel genetic modifiers of HD by combining information from the HD Research Crossroads database with previous genome-wide linkage studies.ResultsOur analyses led to a comprehensive identification of molecular mechanisms associated with HD. Remarkably, we not only recovered processes and pathways, which have frequently been linked to HD (such as cytotoxicity, apoptosis, and calcium signaling), but also found strong indications for other potentially disease-relevant mechanisms that have been less intensively studied in the context of HD (such as the cell cycle and RNA splicing, as well as Wnt and ErbB signaling). For follow-up studies, we provide a regularly updated compendium of molecular mechanism, that are associated with HD, at http://hdtt.sysbiolab.eu Additionally, we derived a candidate set of 24 novel genetic modifiers, including histone deacetylase 3 (HDAC3), metabotropic glutamate receptor 1 (GRM1), CDK5 regulatory subunit 2 (CDK5R2), and coactivator 1ß of the peroxisome proliferator-activated receptor gamma (PPARGC1B).ConclusionsThe results of our study give us an intriguing picture of the molecular complexity of HD. Our analyses can be seen as a first step towards a comprehensive list of biological processes, molecular functions, and pathways involved in HD, and may provide a basis for the development of more holistic disease models and new therapeutics.

Project description:Background and objectivesThe variable CAG repeat expansion in the huntingtin gene and its inverse relationship to motor dysfunction onset are fundamental features of Huntington disease (HD). However, the wider phenotype (including non-motor features) at particular CAG lengths, ages, and functional levels is less well-characterized. The large number of participants in the Enroll-HD observational study enables the development of a phenotype atlas that summarizes the range and distribution of HD phenotypes, including outliers and possible clusters, with respect to various CAG repeat lengths, age ranges, and declining functional levels.MethodsEnroll-HD is an ongoing prospective longitudinal observational study that collects natural history data, releasing periodic data sets, in people with HD (PwHD) and controls. Core assessments at annual visits focus on behavioral, cognitive, motor, and functional status. Periodic data set 5, used for the development of the first iteration of the Enroll-HD Phenotype Atlas (EHDPA), included all eligible data collected through October 31, 2020. The atlas is based on subsets (cells) of descriptive data for all motor, cognitive, psychiatric, and functional measures that are routinely collected at most Enroll-HD sites, analyzed by single CAG lengths and 5-year age blocks.ResultsData from 42,840 visits from 15,982 unique PwHD were available for analysis. At baseline, participants had a mean ± SD age of 48.9 ± 13.9 years and CAG repeat length of 43.4 ± 3.6 and 54.1% were female. The EHDPA includes 223 age-by-CAG subsets for CAG repeats between 36 and 69 with five-year age brackets starting from 20-24 years up to 85-89 years. The atlas can be browsed at enroll-hd.org/for-researchers/atlas-of-hd-phenotype/.DiscussionThe EHDPA summarizes the spectrum and distribution of HD phenotypes, including outliers and possible clusters, in all domains of disease involvement for the range of CAG repeat lengths, ages, and functional levels. Its availability in an easy-to-use online format will assist clinicians in tracking disease progression in PwHD by identifying phenotypic features most associated with loss of function and enabling conversations related to prognosis. The observable patterns in the EHDPA should also catalyze more formal multidomain characterization of motor, cognitive, and psychiatric progression and their relationships to functional decline and disease modifiers.Trial registration informationEnroll-HD is registered with clinicaltrials.gov: NCT01574053.

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Data Analytics from Enroll-HD, a Global Clinical Research Platform for Huntington's Disease.

Publications

Data Analytics from Enroll-HD, a Global Clinical Research Platform for Huntington's Disease.

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